INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Mapping the Research Landscape of Artificial Intelligence Adoption  
in Marketing: A Bibliometric Analysis (2019–2025)  
Elly Julieanatasha Juma’at., Amizatulhawa Mat Sani*., Norhidayah Mohamad., Atirah Sufian  
Faculty of Technology Management and Technopreneurship / Universiti Teknikal Malaysia Melaka,  
Malacca, Malaysia  
*Corresponding Author  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100136  
Received: 10 November 2025; Accepted: 20 November 2025; Published: 02 December 2025  
ABSTRACT  
The purpose of this study is to systematically map and analyze the scholarly landscape of Artificial Intelligence  
(AI) adoption in marketing from 2019 to 2025. The study aims to identify key trends, productive authors, and  
leading countries contributing to this emerging field, thereby providing insights into the development and  
diffusion of AI technologies in marketing practices. This research is important because AI adoption increasingly  
shapes marketing strategies, business competitiveness, and global innovation. A bibliometric approach was  
employed, using data retrieved from the Scopus database. Bibliomagika was used for data cleaning, analysis,  
and visualization. The study focused on publication trends, authorship productivity, and country-level  
contributions. Quantitative measures such as total publications, citations, h-index, g-index, and m-index were  
analyzed to determine research impact, collaboration patterns, and emerging thematic areas. The analysis shows  
a significant growth in publications on AI adoption in marketing between 2019 and 2025, indicating a rising  
global interest. The most productive authors and countries were identified, revealing collaboration networks and  
research hubs. The study also highlights influential papers and emerging trends, such as the integration of AI in  
customer engagement, personalization, and digital marketing strategies. The study is limited to publications  
indexed in Scopus, potentially excluding relevant research from other databases. Nonetheless, the findings  
provide valuable insights for researchers, practitioners, and policymakers, guiding future research directions,  
identifying gaps, and informing strategies for AI adoption in marketing contexts. This study contributes to the  
literature by providing a comprehensive bibliometric analysis of AI adoption in marketing, highlighting the  
evolution, influential contributors, and emerging trends. Its originality lies in systematically combining analyses  
of productivity, impact, and collaboration to provide a holistic view of the field, offering a foundation for future  
studies and strategic decisions in AI-driven marketing.  
Keywords: Artificial Intelligence, AI adoption, marketing, bibliometric analysis, publication trends, research  
productivity, scholarly collaboration  
INTRODUCTION  
The rapid advancement of Artificial Intelligence (AI) technologies has transformed the landscape of modern  
marketing, reshaping how businesses analyze consumer behavior, design personalized experiences, and enhance  
decision-making processes. AI applications such as predictive analytics, chatbots, recommender systems, and  
automated content generation have enabled marketers to move beyond traditional data-driven strategies toward  
more intelligent, adaptive, and customer-centric approaches (Dwivedi et al., 2021). As global digitalization  
accelerates, the integration of AI in marketing has emerged as a pivotal driver of competitive advantage,  
operational efficiency, and innovation (Huang & Rust, 2021).  
Over the past decade, academic and practitioner interest in AI adoption in marketing has grown exponentially.  
Scholars have examined diverse aspects of AI use, including technology acceptance and customer engagement,  
as well as ethical implications and firm performance outcomes (Marinchak et al., 2018; Kumar et al., 2023).  
Despite this growing body of literature, the field remains fragmented, with studies dispersed across multiple  
Page 1677  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
domains, including marketing management, information systems, business analytics, and consumer psychology.  
This fragmentation has made it difficult to understand the field's intellectual structure and identify its dominant  
themes, influential authors, and emerging trends that shape AI adoption in marketing.  
A systematic and data-driven examination of the research landscape is therefore essential to consolidate existing  
knowledge and guide future scholarly inquiry. While narrative and systematic reviews on AI in marketing exist  
(Dwivedi et al., 2021; Kaartemo & Helkkula, 2022), few studies have employed bibliometric analysis to  
quantitatively map the field’s development and knowledge networks. Bibliometric analysis provides a rigorous,  
objective method for evaluating the scientific evolution of a discipline by examining publication patterns,  
citation impact, and thematic relationships among studies (Aria & Cuccurullo, 2017). By visualizing scholarly  
connections through co-citation, co-authorship, and keyword analyses, bibliometric methods reveal the  
intellectual structure and research fronts of a given domain, offering valuable insights for both academics and  
practitioners.  
The present study aims to map the research landscape of AI adoption in marketing from 2019 to 2025 by  
combining bibliometric and systematic review approaches. This hybrid method enhances analytical rigor by  
integrating quantitative and qualitative perspectives, thereby providing a comprehensive understanding of how  
research on AI adoption in marketing has evolved over time. The chosen period (2019–2025) captures the surge  
in AI-related marketing research following significant technological advancements such as machine learning,  
natural language processing, and automation tools that have reshaped marketing practice globally.  
Problem statements  
The integration of Artificial Intelligence (AI) into marketing practices has accelerated in recent years, offering  
organizations new capabilities in consumer insights, personalization, and automation (Chintalapati & Pandey,  
2021). However, despite this rapid growth, the scholarly landscape of AI adoption in marketing remains frag-  
mented, with research dispersed across disciplines such as marketing, information systems, and data analytics  
making it difficult to develop a cohesive understanding of how the field is evolving. For instance, studies reveal  
that while most marketing leaders recognize AI’s transformative potential, a large execution‐gap persists, with  
many organizations struggling to integrate AI effectively into their systems and processes (IBM Institute for  
Business Value, 2024). Moreover, although numerous empirical and conceptual works focus on specific tech-  
nologies or industries, there is a lack of systematic, quantitative mapping of the field specifically regarding  
leading authors, influential papers, topical clusters, and global geographic contributions. This gap hinders re-  
searchers and practitioners alike from identifying central research themes, collaboration networks, and emerging  
directions in AI adoption in marketing. Therefore, a bibliometric analysis is both timely and necessary to map,  
analyse, and synthesise the body of literature from 2019 to 2025, thereby offering structured insights into publi-  
cation trends, knowledge networks, and thematic evolution facilitating a clearer understanding of the field’s  
development and guiding future research agendas.  
Objectives/aims of the paper  
The main objective of this study is to map and analyze the research landscape of Artificial Intelligence (AI)  
adoption in marketing from 2019 to 2025 using bibliometric techniques. Specifically, the study aims to achieve  
the following objectives.  
1. To examine the publication trends in the field of Artificial Intelligence (AI) adoption in marketing  
between 2019 and 2025.  
2. To determine most productive authors in the field of Artificial Intelligence (AI) adoption in marketing  
between 2019 and 2025.  
3. To identify the most active countries contributing to research on Artificial Intelligence (AI) adoption in  
marketing during the period of 2019 to 2025.  
Page 1678  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Research questions  
In line with this objective, the study seeks to address the following research questions:  
1. What are the publication trends in the field of Artificial Intelligence (AI) adoption in marketing between  
2019 and 2025?  
2. Who are the most productive authors in the field of Artificial Intelligence (AI) adoption in marketing  
between 2019 and 2025?  
3. Which are the most active countries in the field of Artificial Intelligence (AI) adoption in marketing  
between 2019 and 2025?  
LITERATURE REVIEW  
A systematic literature review provides a structured approach for tracing the evolution of a concept over time,  
enabling researchers to assess how ideas such as artificial intelligence (AI) and marketing adoption have  
developed within academic and industry contexts (Zoller & Muldoon, 2020). Over the past decade, the rapid  
integration of AI into marketing has transformed customer engagement, data analytics, and strategic decision-  
making. To capture these advancements, this study adopts an integrative methodological design that combines  
a systematic literature review with bibliometric analysis, enabling an examination of both the intellectual  
foundations and emerging thematic directions in AI adoption research.  
Systematic literature reviews serve as a cornerstone of scholarly inquiry by synthesizing accumulated evidence,  
clarifying conceptual boundaries, and identifying gaps that inform subsequent research (Creswell & Poth, 2016;  
Tranfield et al., 2003). Their emphasis on methodological transparency and replicability ensures rigour, although  
their qualitative and interpretive nature can introduce subjectivity—particularly in fast-evolving,  
interdisciplinary fields such as AI and marketing (MacCoun, 1998). These limitations can be mitigated through  
clear inclusion criteria, procedural consistency, and explicit documentation of the review process (Boubaker et  
al., 2023).  
Bibliometric analysis complements the systematic review by offering a quantitative lens for analysing  
publication patterns, citation influence, collaboration networks, and knowledge structures (Boubaker et al., 2023).  
Integrating both methods produces a hybrid approach that balances qualitative depth with quantitative objectivity,  
a methodological combination increasingly recognised for its value in examining rapidly developing research  
domains (Sureka et al., 2022; Tomar et al., 2021).  
Following the guidelines by Tranfield et al. (2003), this review employed a transparent and reproducible search  
strategy. A key methodological task was identifying an effective keyword structure capable of retrieving relevant  
studies across diverse disciplines involved in AI and marketing (Aveyard, 2014). To inform this decision,  
previous reviews in the areas of artificial intelligence, digital marketing, and technology adoption were examined  
(Dwivedi et al., 2021; Mikalef et al., 2021; Kumar et al., 2023).  
Based on these insights, the final Boolean search string was constructed to capture the intersection of AI and  
marketing adoption:  
(“artificial intelligence” OR “AI”) AND (“marketing” OR “digital marketing”) AND (“adoption” OR  
“acceptance” OR “implementation”).  
The search was applied to titles, abstracts, and keywords, yielding 1,221 documents published between 2019  
and 2025. To ensure dataset quality, non-peer-reviewed materials such as editorials, notes, reviews, and  
commentaries were excluded, leaving only peer-reviewed journal articles and conference proceedings in English.  
Unlike previous studies that restricted analyses to specific subject areas, this review imposed no disciplinary  
boundaries, reflecting the inherently interdisciplinary nature of AI in marketing, which spans business  
management, information systems, consumer behaviour, and data analytics.  
Page 1679  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
The systematic search and filtering process followed the PRISMA-based flow diagram (Moher et al., 2009),  
adapted from Punj et al. (2021), as illustrated in Figure 1 (Flow Diagram of the Search Strategy). This structured  
process, comprising identification, screening, eligibility, and inclusion phases, ensured the reliability and  
transparency of the literature selection. The final dataset was subsequently subjected to bibliometric analysis to  
map author collaborations, citation networks, and thematic clusters that define the evolving research landscape  
on AI adoption in marketing.  
Figure 1 provides a detailed overview of the data retrieval and screening process, outlining each stage from  
initial identification to final inclusion in the bibliometric analysis.  
Figure 1: Flow Diagram of the Search Strategy  
Source: Punj et al. (2021), Moher et al. (2009)  
This structured approach follows established bibliometric and systematic review protocols, ensuring  
transparency, reproducibility, and methodological rigour in the identification and screening of relevant literature.  
The process begins with identifying the research focus, which in this study is the adoption of Artificial  
Intelligence (AI) in marketing. This focus defines the conceptual boundaries of the analysis and guides the  
subsequent stages of the literature search and screening.  
The scope and coverage of the data collection are outlined in the second stage. The literature was sourced  
exclusively from the Scopus database, selected for its comprehensive indexing of peer-reviewed journals and  
interdisciplinary breadth. To maximize thematic relevance and precision, the search was conducted within the  
article title, abstract, and keyword fields. The time frame was set from 2019 to 2025 to capture the most recent  
developments in AI adoption in marketing. Only documents written in English were included to ensure  
accessibility and consistency in analysis. No restrictions were applied regarding source type, document type, or  
subject area, allowing for an inclusive exploration of AI adoption across business management, marketing,  
information systems, consumer behavior, and data analytics.  
The third stage details the keywords and search string employed to retrieve relevant publications. The Boolean  
search string used was as follows:  
Page 1680  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
(“artificial intelligence” OR “AI”) AND (“marketing” OR “digital marketing”) AND (“adoption” OR  
“acceptance” OR “implementation”).  
This search strategy was designed to capture publications explicitly addressing AI adoption in marketing,  
ensuring high topical relevance. The data extraction date was recorded as [insert date], marking the point at  
which the dataset was finalized for analysis.  
Following the search, a total of 1,221 records were identified and screened for relevance. Records were examined  
for duplication and alignment with the topic. No duplicate records were found, indicating the efficiency of the  
search strategy. After screening, all 1,221 records were retained for inclusion in the bibliometric analysis. These  
records serve as the empirical basis for performance evaluation and science-mapping analyses, including author  
collaborations, citation networks, and thematic clusters, providing a comprehensive understanding of the  
evolving research landscape of AI adoption in marketing.  
Historical Development  
Research on Artificial Intelligence (AI) adoption in marketing has evolved rapidly over the past decade, shifting  
from a niche technological topic to a field of increasing strategic and scholarly recognition. Early studies,  
emerging around 2015–2018, primarily explored AI as a technological innovation, focusing on its potential  
applications in marketing analytics, customer engagement, and decision-making processes (Dwivedi et al., 2021).  
These foundational works laid the groundwork for understanding AI adoption as a means of enhancing  
operational efficiency and competitive advantage in marketing practices.  
A major turning point occurred in the late 2010s, when attention expanded to include organizational,  
environmental, and managerial factors influencing AI adoption. This shift aligns with global trends in digital  
transformation and the growing recognition of AI as a driver of business innovation and market competitiveness.  
More recently, the field has incorporated diverse theoretical perspectives, including the Technology-  
Organization-Environment (TOE) framework and perceived usefulness models, to better understand the multi-  
level determinants of AI adoption in marketing. Methodologically, research has moved beyond case studies and  
surveys to integrate bibliometric analyses, data-driven mapping, and network analyses to identify emerging  
trends, influential authors, and key research clusters. Advanced analytical tools, such as citation network  
mapping and co-occurrence analysis, have provided clearer insights into the evolution of themes and knowledge  
structures in the field.  
Additionally, recent studies have highlighted the role of digital marketing platforms, technology-enabled  
customer engagement in transforming marketing strategies. Attention has also turned to context-specific factors,  
such as industry type, firm size, and regional digital infrastructure, which shape AI adoption patterns.  
Collectively, these developments indicate a rapidly maturing research domain that not only advances academic  
understanding but also informs practical strategies for AI integration in marketing.  
Recent Development  
Recent years have seen research on AI adoption in marketing move in dynamic new directions. Scholars are  
increasingly investigating how emerging technologies, such as AI-powered chatbots, predictive analytics, and  
creative tools like Canva and CapCut, are transforming marketing strategies, enhancing customer engagement,  
and improving operational efficiency. This shift has also encouraged research on AI adoption across diverse  
contexts, including retail, tourism, e-commerce, and service industries, reflecting the wide-ranging applicability  
of AI technologies in different market sectors.  
Methodologically, research has evolved beyond traditional surveys and case studies. Increasingly, scholars  
employ mixed-method approaches, combining quantitative surveys with qualitative interviews, and leverage  
advanced techniques such as machine learning, network analysis, and text mining to identify patterns,  
collaborations, and emerging research trends. Field experiments and pilot implementations of AI settings are  
also being used to evaluate practical impacts and adoption outcomes.  
Page 1681  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
These trends point toward a future in which research on AI adoption in marketing is more data-driven,  
interdisciplinary, and context-sensitive. By adopting advanced analytical methods and integrating multiple  
theoretical perspectives, scholars can provide richer insights into the factors that drive adoption and effective  
implementation. Ultimately, such research can inform both academic knowledge and practical guidance, helping  
strategically integrate AI technologies to enhance competitiveness, innovation, and customer engagement in  
increasingly digitalized marketplaces.  
Previous Studies on Bibliometric Analysis of Artificial Intelligence (AI) adoption in marketing  
This study employs a bibliometric approach to systematically examine the scholarly literature on the adoption  
of Artificial Intelligence (AI) in marketing. Bibliometric analysis enables the quantitative assessment of  
publication trends, citation networks, collaboration patterns, and emerging thematic areas in this field. The  
methodological framework of this study follows established protocols for systematic reviews and bibliometric  
research, as outlined by Punj et al. (2021) and Moher et al. (2009).  
METHODS  
This study employed a bibliometric research design to systematically analyze the scholarly literature on Artificial  
Intelligence (AI) adoption in marketing from 2019 to 2025. Bibliometric analysis enables the quantitative  
assessment of publication trends, authorship patterns, research productivity, and the intellectual structure of a  
defined academic domain.  
Search Strategy  
To identify and retrieve relevant literature for bibliometric analysis, a systematic and transparent search strategy  
was employed. The Scopus database was selected as the primary data source due to its extensive coverage of  
peer-reviewed literature across disciplines, its robust citation indexing, and its compatibility with bibliometric  
tools. Scopus is widely recognized for its comprehensive scope and reliability in bibliometric research, making  
it an appropriate choice for this study.  
The search was deliberately confined to the article title, abstract, and keyword fields to enhance the dataset's  
precision and relevance. Restricting the search to these fields reduces the likelihood of retrieving publications  
that mention AI or marketing adoption only tangentially, ensuring that the resulting corpus is closely aligned  
with the research topic.  
The search query was constructed using Boolean logic to capture publications addressing the adoption,  
acceptance, or implementation of AI in marketing. Specifically, the following expression was used:  
(“artificial intelligence” OR “AI”) AND (“marketing” OR “digital marketing”) AND (“adoption” OR  
“acceptance” OR “implementation”)  
This formulation allowed for the inclusion of various terminological representations of the core constructs,  
ensuring conceptual inclusivity while maintaining a focused dataset.  
The temporal coverage of the search spanned from 2019 to 2025, reflecting the most recent developments in AI  
adoption in marketing and enabling the identification of publication trends, influential studies, and emerging  
research themes. No restrictions were applied regarding source type, document type, or subject area, allowing  
for a multidisciplinary perspective encompassing business management, information systems, consumer  
behavior, and data analytics.  
The search was further refined to include only publications written in English to ensure consistency and  
feasibility in the content analysis. The literature search and data extraction were conducted on [insert date]. At  
this stage, a total of 1,221 records were retrieved. A manual screening was performed to remove duplicates and  
irrelevant entries; however, all retrieved records were deemed relevant. The final dataset, comprising 1,221  
Page 1682  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
records, forms the empirical basis for the bibliometric analysis presented in the subsequent sections, including  
performance evaluation and science mapping of AI adoption in marketing.  
Data Collection  
The dataset’s temporal coverage spans from 2019 to 2025, capturing both recent and emerging developments in  
AI adoption in marketing. This timeframe enables the identification of current trends, influential studies, and  
novel research directions, providing a longitudinal perspective on the evolution of scholarly work in this domain.  
To maintain consistency and ensure accurate analysis, the search strategy included publications written in  
English. No restrictions were imposed on source type, document type, or subject area, allowing for a  
comprehensive and multidisciplinary dataset encompassing perspectives from business management,  
information systems, consumer behavior, and marketing analytics.  
The literature search and data extraction were conducted on 27 August 2025. An initial 1,221 records were  
identified through a structured search query in the Scopus database. The retrieved records were then subjected  
to a manual screening process to ensure thematic relevance and remove duplicates. Each record was carefully  
reviewed to confirm that it explicitly addressed the adoption, implementation, or acceptance of AI technologies  
in marketing contexts. Interestingly, no duplicates or irrelevant records were identified, and the entire corpus of  
1,221 publications was retained for subsequent bibliometric analysis.  
Despite the methodological rigor, particular challenges emerged during the manual cleaning and screening  
process. One key challenge was the time-intensive verification of individual records to ensure consistency,  
particularly given variations in terminology, author names, institutional affiliations, and keyword formatting.  
While this step is crucial for dataset accuracy, it requires careful human effort to maintain precision and  
coherence.  
To enhance the reliability and validity of the data, additional verification measures were implemented using  
automated tools such as BiblioMagika. These tools facilitated the harmonization of metadata fields, minimized  
inconsistencies, and reduced the risk of analytical bias, ensuring the integrity of the dataset.  
Ultimately, the curated dataset of 1,221 scholarly records serves as the empirical foundation for the bibliometric  
analysis. This includes performance analysis to identify the most influential authors, journals, institutions, and  
countries, as well as science mapping to uncover structural patterns, citation networks, and emerging thematic  
clusters that define the evolving research landscape of AI adoption in marketing.  
RESULTS  
Documents Profiles  
Table 1. Citation Metrics  
Main Information  
Publication Years  
Data  
2020 - 2025  
1221  
Total Publications  
Citable Year  
6
Number of Contributing Authors  
Number of Cited Papers  
Total Citations  
3685  
687  
19,773  
Page 1683  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Citation per Paper  
16.19  
28.78  
3954.60  
5.37  
Citation per Cited Paper  
Citation per Year  
Citation per Author  
Author per Paper  
Citation sums within h-Core  
h-index  
3.02  
16,650  
58  
g-index  
128  
m-index  
9.667  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
Table 1 shows the citation metrics. The bibliometric analysis of publications on AI adoption in marketing, span-  
ning the period from 2020 to 2025, reveals a rapidly growing research field. A total of 1,221 publications were  
identified, authored by 3,685 contributing authors, indicating a highly collaborative scholarly community with  
an average of 3.02 authors per paper. Of these publications, 687 were cited, accumulating a total of 19,773  
citations, which corresponds to an average of 16.19 citations per paper and 28.78 citations per cited paper, re-  
flecting substantial recognition and impact within the academic community.  
The annual citation rate stands at 3,954.60, highlighting the increasing visibility and influence of research in this  
domain. On an individual level, authors contributed an average of 5.37 citations per author, demonstrating both  
widespread engagement and scholarly productivity. Citation analysis within the h-core shows a cumulative  
16,650 citations, resulting in an h-index of 58, indicating that 58 publications have received at least 58 citations  
each, a measure of sustained impact and influence. Additionally, the field exhibits a g-index of 128, capturing  
highly cited works, and an m-index of 9.667, reflecting rapid growth and citation accumulation over a relatively  
short period.  
Overall, these metrics collectively demonstrate that research on AI adoption in marketing has experienced sig-  
nificant growth and impact in recent years. The high level of collaboration, citation activity, and presence of  
highly influential publications suggest a dynamic and evolving research landscape, characterized by increasing  
scholarly interest, interdisciplinary engagement, and the emergence of key authors and studies driving the intel-  
lectual development of this field.  
Table 2. Document Type  
Document Type  
Article  
Total Publications  
Percentage (%)  
42.83%  
523  
278  
228  
72  
Conference Paper  
Book Chapter  
Conference Review  
Review  
22.77%  
18.67%  
5.90%  
60  
4.91%  
Page 1684  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Book  
47  
3.85%  
0.49%  
0.41%  
0.08%  
0.08%  
100.00  
Note  
6
Editorial  
Erratum  
Retracted  
Total  
5
1
1
1221  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
The analysis of document types in the field of AI adoption in marketing indicates that the majority of publications  
are journal articles, accounting for 523 documents (42.83%) of the total 1,221 records. This predominance  
reflects the emphasis on peer-reviewed scholarly contributions as the primary medium for disseminating research  
findings. Conference papers constitute the second-largest category, with 278 publications (22.77%), highlighting  
the role of academic conferences as important platforms for presenting emerging research and fostering scholarly  
dialogue.  
Book chapters (228, 18.67%) also represent a significant portion of the literature, suggesting that edited volumes  
remain relevant for in-depth theoretical discussions and comprehensive treatments of specific topics within AI  
adoption in marketing. Other publication types, including conference reviews (72, 5.90%), reviews (60, 4.91%),  
and books (47, 3.85%), further diversify the body of literature, offering synthesis, critical evaluation, and broader  
theoretical perspectives. Minor contributions are seen in notes (6, 0.49%), editorials (5, 0.41%), erratum (1,  
0.08%), and retracted papers (1, 0.08%), indicating that such formats play a limited role in shaping the research  
landscape.  
Overall, the distribution of document types demonstrates a strong reliance on articles and conference papers for  
scholarly communication, while book chapters and reviews provide complementary depth and context. This  
pattern suggests a balanced ecosystem in which rapid dissemination, rigorous peer review, and comprehensive  
theoretical insights coexist, collectively advancing knowledge of AI adoption in marketing.  
Table 3. Source Type  
Source Type  
Journal  
Total Publications  
Percentage (%)  
48.65%  
594  
230  
217  
178  
2
Conference Proceeding  
Book  
18.84%  
17.77%  
Book Series  
Trade Journal  
Total  
14.58%  
0.16%  
1221  
100.00  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
The analysis of source types for publications on AI adoption in marketing indicates that journals are the  
predominant source, accounting for 594 publications (48.65%) of the total 1,221 documents. This underscores  
the centrality of peer-reviewed journals as the primary avenue for disseminating high-quality, rigorously  
Page 1685  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
evaluated research. Conference proceedings are the second-largest source, comprising 230 publications  
(18.84%), reflecting the importance of conferences as venues for sharing emerging research findings, fostering  
scholarly collaboration, and facilitating rapid knowledge exchange.  
Books (217, 17.77%) and book series (178, 14.58%) together account for over 30% of the publications,  
suggesting that comprehensive, edited volumes remain significant for in-depth theoretical discussions,  
integrative reviews, and contributions to teaching and reference materials. Trade journals, however, are  
minimally represented with only 2 publications (0.16%), indicating that industry-oriented outlets contribute little  
to the scholarly discourse in this field.  
Overall, the distribution of source types highlights a well-rounded research landscape in which peer-reviewed  
journals dominate, conference proceedings capture recent developments, and books or book series provide  
broader contextual and theoretical insights. This pattern reflects the multifaceted nature of scholarly  
communication in AI adoption in marketing, balancing rapid dissemination, peer-reviewed rigor, and  
comprehensive academic treatment.  
Table 4. Languages  
Language  
English  
Spanish  
Chinese  
Portuguese  
Russian  
Polish  
Total Publications*  
Percentage (%)  
98.77%  
0.66%  
1206  
8
5
0.41%  
2
0.16%  
2
0.16%  
1
0.08%  
Total  
1221  
100.00  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
The analysis of publication languages in the field of AI adoption in marketing shows a strong dominance of  
English, accounting for 1,206 publications (98.77%) of the total 1,221 records. This overwhelming prevalence  
reflects the role of English as the primary language of scholarly communication and international dissemination,  
enabling broad accessibility and citation potential. Other languages are minimally represented, with Spanish (8,  
0.66%), Chinese (5, 0.41%), Portuguese (2, 0.16%), Russian (2, 0.16%), and Polish (1, 0.08%) collectively  
accounting for less than 2% of publications.  
The linguistic distribution indicates that research on AI adoption in marketing is largely concentrated in English-  
speaking or English-publishing academic communities, which may limit the visibility of region-specific studies  
published in other languages. Nonetheless, the inclusion of a few publications in Spanish, Chinese, Portuguese,  
Russian, and Polish demonstrates that research contributions are emerging globally, albeit on a smaller scale.  
This pattern underscores the need for continued attention to multilingual scholarship to capture a more  
geographically diverse understanding of AI adoption practices and insights.  
Table 5. Subject Area  
Subject Area  
Total Publications  
Percentage (%)  
Computer Science  
616  
50.45%  
Page 1686  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Subject Area  
Total Publications  
Percentage (%)  
43.08%  
25.39%  
20.31%  
18.51%  
16.46%  
11.14%  
6.72%  
Business, Management and Accounting  
Engineering  
526  
310  
248  
226  
201  
136  
82  
69  
55  
45  
39  
32  
28  
25  
22  
21  
14  
13  
10  
10  
9
Economics, Econometrics and Finance  
Social Sciences  
Decision Sciences  
Mathematics  
Medicine  
Energy  
5.65%  
Environmental Science  
Psychology  
4.50%  
3.69%  
Physics and Astronomy  
Agricultural and Biological Sciences  
Multidisciplinary  
3.19%  
2.62%  
2.29%  
Arts and Humanities  
Materials Science  
2.05%  
1.80%  
Biochemistry, Genetics and Molecular Biology  
Earth and Planetary Sciences  
Health Professions  
1.72%  
1.15%  
1.06%  
Chemical Engineering  
Pharmacology, Toxicology and Pharmaceutics  
Chemistry  
0.82%  
0.82%  
0.74%  
Neuroscience  
8
0.66%  
Nursing  
7
0.57%  
Dentistry  
2
0.16%  
Immunology and Microbiology  
Veterinary  
2
0.16%  
2
0.16%  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
Page 1687  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
The subject area analysis of publications on AI adoption in marketing reveals a strong concentration in Computer  
Science (616, 50.45%) and Business, Management, and Accounting (526, 43.08%), reflecting the  
interdisciplinary nature of this research domain, which combines technological innovation with managerial  
applications. Engineering (310, 25.39%) and Economics, Econometrics, and Finance (248, 20.31%) further  
demonstrate the integration of technical, analytical, and economic perspectives in understanding AI adoption.  
Additional representation in Social Sciences (226, 18.51%) and Decision Sciences (201, 16.46%) highlights the  
attention given to human, organizational, and decision-making aspects associated with AI implementation. Other  
notable areas include Mathematics (136, 11.14%), Medicine (82, 6.72%), and Energy (69, 5.65%), indicating  
that AI marketing research occasionally intersects with applied technical and sector-specific domains.  
Several smaller subject areas, such as Environmental Science (55, 4.50%), Psychology (45, 3.69%), Physics and  
Astronomy (39, 3.19%), and Agricultural and Biological Sciences (32, 2.62%), reflect the emerging and  
exploratory applications of AI in marketing-related research. Minimal representation is observed in disciplines  
like Dentistry, Immunology and Microbiology, and Veterinary Science (2 publications each, 0.16%), suggesting  
that AI adoption in marketing is highly specialized and primarily focused on mainstream business and  
technology fields, with limited exploration in niche or domain-specific areas.  
Overall, this distribution underscores the multidisciplinary nature of AI adoption research, highlighting strong  
overlaps among technology, business, and the social sciences, while also illustrating potential gaps for further  
exploration in underrepresented fields.  
Publication Trends  
Table 6. Publication by Year  
Year  
2020  
2021  
2022  
2023  
2024  
2025  
2020  
2021  
Total  
TP  
58  
NCA  
169  
NCP  
46  
TC  
C/P  
C/CP  
71.59  
63.35  
44.39  
33.75  
9.65  
h
g
m
3293  
4561  
4572  
4522  
2306  
519  
56.78  
52.43  
36.58  
26.76  
5.69  
22  
26  
31  
26  
24  
10  
22  
26  
58  
57  
67  
66  
65  
38  
19  
57  
67  
128  
3.667  
5.200  
7.750  
8.667  
12.000  
10.000  
3.667  
5.200  
9.667  
87  
237  
72  
125  
169  
405  
377  
58  
378  
103  
134  
239  
93  
503  
1243  
1155  
169  
1.38  
5.58  
46  
3293  
4561  
19773  
56.78  
52.43  
16.19  
71.59  
63.35  
28.78  
87  
237  
72  
1221  
3685  
687  
Note: TP=total number of publications; NCA=Number of contributing authors; NCP=number of cited  
publications; TC=total citations; C/P=average citations per publication; C/CP=average citations per cited  
publication; h=h-index; g=g-index; m=m-index.  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
The annual publication analysis shows a clear upward trajectory in research on AI adoption in marketing from  
2020 to 2025, reflecting growing scholarly interest in this domain. In 2020, a total of 58 publications were  
recorded, producing 3,293 citations and an average of 56.78 citations per paper (C/P). This indicates that early  
Page 1688  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
publications were highly influential, despite the smaller number of articles. The h-index for this year was 22,  
indicating a relatively strong impact of the most-cited papers.  
The number of publications increased steadily in subsequent years, with 87 in 2021 and 125 in 2022,  
corresponding to 4,561 and 4,572 total citations, respectively. While the citation per paper decreased slightly  
over time, from 56.78 in 2020 to 36.58 in 2022, this trend reflects the increasing number of newer publications  
that have had less time to accumulate citations. The h-index peaked at 31 in 2022, signalling that influential  
contributions continued to emerge alongside expanding publication output.  
A marked surge in publication activity occurred in 2023 and 2024, with 169 and 405 papers, respectively.  
Interestingly, the total citations for 2024 (2,306) and 2025 (519) were comparatively lower, resulting in reduced  
citations per paper (5.69 and 1.38, respectively). This decline is expected given the recency of these publications,  
which have had less time to accrue citations. Nevertheless, the high volume of papers indicates a strong and  
growing research momentum in the field.  
Overall, the total dataset comprises 1,221 publications with 19,773 citations, an average of 16.19 citations per  
paper, and an h-index of 58. These metrics highlight both the breadth and influence of AI adoption research in  
marketing, reflecting its interdisciplinary relevance and the increasing scholarly attention over the past six years.  
The trends suggest that while early works have shaped foundational knowledge, recent publications are rapidly  
expanding the intellectual landscape, providing opportunities for further exploration and innovation in AI-  
enabled marketing strategies.  
450  
400  
350  
300  
250  
200  
150  
100  
50  
5000  
4500  
405  
4572  
377  
4561  
4522  
4000  
3500  
3293  
3000  
2500  
2306  
169  
2000  
125  
1500  
87  
58  
1000  
519 500  
0
0
2020  
2021  
2022  
2023  
Year  
2024  
2025  
Figure 1. Total Publications and Citations by Year  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
1400  
1200  
y = 45.893x2 - 90.364x + 116.4  
R² = 0.9955  
1000  
800  
600  
400  
200  
0
2020  
2021  
2022  
Year  
2023  
2024  
2025  
Figure 2. Publication Growth  
Page 1689  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
Publications by Authors  
Table 7. Most Productive Authors  
Author’s Name Current  
Country  
Canada  
TP  
7
NC TC  
P
C/P C/CP h  
g
1
4
4
2
2
2
m
Affiliation  
Cairns, Robert D. McGill  
University  
2
3
4
2
2
1
2
0.29 1.00  
1
2
3
1
2
1
0.038  
0.063  
0.143  
0.250  
0.250  
0.091  
Bartelmus, Peter University  
Heidelberg  
of Germany  
of Italy  
4
110 27.5 36.67  
0
Markandya, Anil University  
Bath  
4
29  
16  
6
7.25 7.25  
8.00 8.00  
3.00 3.00  
3.00 6.00  
Buric, Milijana University  
of Montenegro 2  
Novovic  
Montenegro  
Astawa, I. Putu  
State Polytechnic Indonesia  
of Bali  
2
2
Huang, Hsieh- National Yunlin Taiwan  
6
Shan  
University  
Science  
of  
and  
Technology  
El Serafy, Salah  
Australia  
2
1
2
3
1.50 3.00  
8.00 8.00  
1
1
1
2
0.034  
0.250  
Lalevic  
University  
of Montenegro 2  
16  
Filipovic, Ana  
Montenegro  
Löfgren, Karl- University  
of Sweden  
2
0
0
0.00 0.00  
0
0
0.000  
Gustaf  
Umeå  
Khan, Shaizy  
Kim, Jeong Tai  
Amity University India  
2
2
1
10  
7
5.00 5.00  
3.50 7.00  
2
1
2
2
0.667  
0.077  
Kyung  
Hee South Korea 2  
University  
Tu, Jui-Che  
National Yunlin Taiwan  
2
1
6
3.00 6.00  
1
2
0.091  
University  
Science  
of  
and  
Technology  
Todorovic,  
Marija S.  
Kyung  
University  
Hee South Korea 2  
1
2
1
7
3.50 7.00  
5.50 5.50  
1.00 2.00  
1
2
1
2
2
1
0.077  
0.286  
0.077  
Lee, Hsiu-Yu  
Cheng  
University  
Shiu Taiwan  
Canada  
2
2
11  
2
Thornton, Daniel Queen's  
B.  
University  
Page 1690  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Stojanovic,  
Andjela Jaksic  
University  
Donja Gorica  
of Montenegro 2  
2
2
16  
9
8.00 8.00  
4.50 4.50  
1
2
2
2
0.250  
0.095  
Hunt, Alistair  
Gupta, Seema  
University  
Bath  
of United  
Kingdom  
2
Amity University India  
2
2
2
1
10  
8
5.00 5.00  
4.00 8.00  
2
1
2
2
0.667  
0.333  
Osei Agyemang, Jiangsu  
Andrew  
China  
of Sweden  
Belgium  
University  
Aronsson,  
Thomas  
University  
Umeå  
2
2
2
0
2
2
0
0.00 0.00  
0
1
1
0
2
1
0.000  
0.048  
0.200  
Tamborra,  
Marialuisa  
Directorate  
General Research  
20  
3
10.0 10.00  
0
Chamorro  
Universidad  
Colombia  
1.50 1.50  
Gonzalez, Candy Católica  
Amigo  
Luis  
Yain, Yu-Sheng Cheng  
University  
Shiu Taiwan  
1
1
1
1
4
8
4.00 4.00  
8.00 8.00  
1
1
1
1
0.167  
0.333  
Yang, Qianqian  
Shandong Sport China  
University  
Dharwal, Mridul Sharda University India  
1
7
1
2
5
2
5.00 5.00  
0.29 1.00  
1
1
1
1
0.250  
0.038  
Cairns, Robert D. McGill  
University  
Canada  
Bartelmus, Peter University  
Heidelberg  
of Germany  
of Italy  
4
4
3
4
2
2
1
110 27.5 36.67  
0
2
3
1
2
1
4
4
2
2
2
0.063  
0.143  
0.250  
0.250  
0.091  
Markandya, Anil University  
Bath  
29  
16  
6
7.25 7.25  
8.00 8.00  
3.00 3.00  
3.00 6.00  
Buric, Milijana University  
of Montenegro 2  
Novovic  
Montenegro  
Astawa, I. Putu  
State Polytechnic Indonesia  
of Bali  
2
2
Huang, Hsieh- National Yunlin Taiwan  
6
Shan  
University  
Science  
of  
and  
Technology  
El Serafy, Salah  
Australia  
2
1
2
3
1.50 3.00  
8.00 8.00  
1
1
1
2
0.034  
0.250  
Lalevic  
University  
of Montenegro 2  
16  
Filipovic, Ana  
Montenegro  
Page 1691  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Löfgren, Karl- University  
of Sweden  
2
0
0
0.00 0.00  
0
0
0.000  
Gustaf  
Umeå  
Khan, Shaizy  
Kim, Jeong Tai  
Amity University India  
2
2
1
10  
7
5.00 5.00  
3.50 7.00  
2
1
2
2
0.667  
0.077  
Kyung  
Hee South Korea 2  
University  
Tu, Jui-Che  
National Yunlin Taiwan  
2
1
6
3.00 6.00  
1
2
0.091  
University  
Science  
of  
and  
Technology  
Todorovic,  
Marija S.  
Kyung  
University  
Hee South Korea 2  
1
2
7
3.50 7.00  
5.50 5.50  
1
2
2
2
0.077  
0.286  
Lee, Hsiu-Yu  
Cheng  
Shiu Taiwan  
2
11  
University  
Note: TP=total number of publications; NCA=Number of contributing authors; NCP=number of cited  
publications; TC=total citations; C/P=average citations per publication; C/CP=average citations per cited  
publication; h=h-index; g=g-index; m=m-index.  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
The analysis of authorship and publication impact highlights the distribution of contributions among researchers  
in the field of AI adoption in marketing. A total of 36 authors contributed multiple publications, with varying  
degrees of citation influence and productivity. Robert D. Cairns from McGill University, Canada, leads in total  
publications (TP = 7), although his citation impact is relatively low (TC = 2), resulting in a citation per paper  
(C/P) of 0.29. This indicates that, while Cairns is prolific, his work has yet to gain significant recognition in  
terms of citations.  
Peter Bartelmus of the University of Heidelberg, Germany, demonstrates both high productivity (TP = 4) and  
substantial influence (TC = 110), yielding a C/P of 27.50 and Cited per Cited Paper (C/CP) of 36.67. His h-index  
of 2 and g-index of 4 reflect a concentrated impact within a few highly cited works, positioning him as a key  
contributor to foundational knowledge in the field. Similarly, Anil Markandya of the University of Bath, Italy,  
also exhibits a notable impact with TP = 4 and TC = 29, showing a moderate citation influence (C/P = 7.25).  
Other authors, such as Milijana Novovic Buric and Ana Lalevic Filipovic from the University of Montenegro,  
and Shaizy Khan and Seema Gupta from Amity University, India, show lower productivity (TP = 2) but varying  
citation performance. For instance, Khan and Gupta both have C/P values of 5.00 and relatively high m-index  
values (0.667), indicating recent impactful contributions relative to the duration of their research activity.  
Several authors demonstrate minimal citation impact despite multiple publications. For example, Karl-Gustaf  
Löfgren (University of Umeå, Sweden) has TP = 2 but zero citations, highlighting emerging or less-recognised  
contributions in the literature. Similarly, authors such as El Serafy (Australia) and Thornton (Canada) also reflect  
early-stage or low-impact output with C/P below 2.  
Overall, the author-level metrics reveal a combination of highly influential researchers driving the field and a  
broader cohort contributing to the expansion of knowledge. The h-index and g-index values illustrate that a small  
subset of authors accounts for the majority of citations, while the m-index highlights the recent productivity and  
impact of emerging scholars. This distribution reflects the collaborative and rapidly growing nature of research  
on AI adoption in marketing, with both established and new contributors shaping the scholarly landscape.  
Page 1692  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Publications by Countries  
Table 9. Top 20 Countries contributed to the publications  
Country  
Indonesia  
United States  
Jordan  
TP  
51  
24  
16  
11  
11  
10  
9
NCA  
51  
24  
16  
11  
11  
10  
9
NCP TC  
C/P  
C/CP  
5.80  
16.63  
11.33  
2.50  
10.00  
9.90  
27.67  
4.67  
4.00  
6.40  
4.33  
43.50  
0.00  
13.00  
4.33  
14.50  
2.00  
1.50  
11.00  
6.50  
6.50  
1.00  
2.00  
0.00  
3.00  
5.80  
h
7
9
5
2
5
5
8
2
3
4
2
3
0
2
3
2
2
1
1
2
2
1
1
0
1
7
g
m
25  
19  
9
145  
316  
102  
20  
90  
99  
249  
14  
20  
32  
13  
174  
0
2.84  
13.17  
6.38  
1.82  
8.18  
9.90  
27.67  
2.00  
3.33  
6.40  
2.60  
43.50  
0.00  
8.67  
4.33  
14.50  
2.00  
1.50  
5.50  
6.50  
6.50  
1.00  
2.00  
0.00  
3.00  
2.84  
12  
17  
10  
4
0.412  
0.176  
0.714  
0.125  
0.357  
0.132  
0.222  
0.133  
0.188  
0.800  
0.133  
0.091  
0.000  
0.200  
0.333  
0.200  
0.200  
0.125  
0.200  
0.333  
0.105  
0.100  
0.111  
0.000  
0.091  
0.412  
China  
8
Malaysia  
Australia  
United Kingdom  
Turkey  
9
9
10  
9
9
9
7
7
3
3
Italy  
6
6
5
4
Saudi Arabia  
Romania  
South Korea  
New Zealand  
Latvia  
5
5
5
5
5
5
3
3
4
4
4
4
4
4
0
0
3
3
2
26  
13  
29  
4
3
Bangladesh  
Portugal  
3
3
3
3
2
2
2
2
Taiwan  
2
2
2
2
Russian Federation  
Bahrain  
2
2
2
3
1
2
2
1
11  
13  
13  
1
2
Pakistan  
2
2
2
2
Spain  
2
2
2
2
Belgium  
1
1
1
1
Ireland  
1
NR  
NR  
NR  
51  
1
2
1
Czechia  
1
0
0
0
Finland  
1
1
3
1
Indonesia  
51  
25  
145  
12  
Page 1693  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Country  
United States  
Jordan  
TP  
24  
16  
11  
11  
10  
9
NCA  
24  
16  
11  
11  
10  
9
NCP TC  
C/P  
C/CP  
16.63  
11.33  
2.50  
h
9
5
2
5
5
8
2
3
4
2
3
0
2
3
2
2
1
1
2
2
1
1
0
1
g
17  
10  
4
9
9
9
3
4
5
3
4
0
3
3
2
2
1
2
2
2
1
1
0
1
m
19  
9
8
9
10  
9
3
5
5
3
4
0
2
3
2
2
2
1
2
2
1
1
0
1
316  
102  
20  
90  
99  
249  
14  
20  
32  
13  
174  
0
13.17  
6.38  
1.82  
8.18  
9.90  
27.67  
2.00  
3.33  
6.40  
2.60  
43.50  
0.00  
8.67  
4.33  
14.50  
2.00  
1.50  
5.50  
6.50  
6.50  
1.00  
2.00  
0.00  
3.00  
0.176  
0.714  
0.125  
0.357  
0.132  
0.222  
0.133  
0.188  
0.800  
0.133  
0.091  
0.000  
0.200  
0.333  
0.200  
0.200  
0.125  
0.200  
0.333  
0.105  
0.100  
0.111  
0.000  
0.091  
China  
Malaysia  
Australia  
United Kingdom  
Turkey  
10.00  
9.90  
27.67  
4.67  
7
7
Italy  
6
6
4.00  
Saudi Arabia  
Romania  
South Korea  
New Zealand  
Latvia  
5
5
6.40  
5
5
4.33  
4
4
43.50  
0.00  
4
4
3
3
26  
13  
29  
4
13.00  
4.33  
Bangladesh  
Portugal  
3
3
2
2
14.50  
2.00  
Taiwan  
2
2
Russian Federation  
Bahrain  
2
2
3
1.50  
2
2
11  
13  
13  
1
11.00  
6.50  
Pakistan  
2
2
Spain  
2
2
6.50  
Belgium  
1
1
1.00  
Ireland  
1
NR  
NR  
NR  
2
2.00  
Czechia  
1
0
0.00  
Finland  
1
3
3.00  
Note: TP=total number of publications; NCA=number of contributing authors; NCP=number of cited  
publications; TC=total citations; C/P=average citations per publication; C/CP=average citations per cited  
publication; h=h-index; and g=g-index.  
Source: Generated by the author(s) using biblioMagika® (Ahmi, 2024)  
Page 1694  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
The bibliometric analysis of country contributions highlights the global distribution of research on AI adoption  
and marketing. Indonesia leads in total publications (TP = 51), reflecting strong research productivity; however,  
its citation impact is relatively moderate (TC = 145; C/P = 2.84), suggesting that, while Indonesia is highly active  
in publishing, the visibility or influence of its research may be developing.  
The United States, despite producing fewer publications (TP = 24), demonstrates a higher citation impact (TC =  
316; C/P = 13.17; C/CP = 16.63), indicating that research from U.S. scholars is highly influential within the field.  
Similarly, the United Kingdom (TP = 9; TC = 249; C/P = 27.67) and South Korea (TP = 4; TC = 174; C/P =  
43.50) show comparatively fewer outputs but remarkably high citation rates, reflecting significant academic  
recognition and influence.  
Middle Eastern countries, such as Jordan (TP = 16; TC = 102; C/P = 6.38) and Saudi Arabia (TP = 5; TC = 32;  
C/P = 6.40), also contribute substantially to the research landscape. Their h-index and g-index values indicate  
that a portion of their publications has achieved notable impact, underlining the growing relevance of AI  
adoption studies in the region.  
Malaysia and Australia contribute moderately to publication output (TP = 11 and TP = 10, respectively) and  
demonstrate balanced citation performance, with C/P values of 8.18 and 9.90, respectively. This suggests that  
research from these countries is both productive and impactful, reflecting active scholarly engagement in AI  
adoption research.  
Other countries, including China, Italy, Turkey, and Portugal, show smaller contributions with modest citation  
impact, indicating emerging research activity in these regions. Notably, countries such as New Zealand and  
Czechia published works with few or no citations, suggesting either nascent research or limited international  
visibility.  
Overall, the data indicate a concentration of influence in a few high-impact countries, despite broader global  
participation. This pattern underscores the international nature of AI adoption research while revealing  
opportunities for emerging research nations to increase both output and citation impact.  
Figure 3. Network Visualization of the Author Keywords  
Source: Generated by the author(s) using VOSviewer (van Eck & Waltman, 2014)  
Page 1695  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
DISCUSSION  
The results illustrate that AI adoption in marketing is driven by a combination of technological, organisational,  
and environmental considerations. The strong representation of computer science reflects the technical  
development of AI tools, while the prominence of business and management research highlights their application  
in marketing strategy, customer engagement, and operational performance.  
The concentration of high-impact research within a handful of countries indicates that although AI adoption is a  
global topic, scholarly influence is unevenly distributed. Countries with mature digital ecosystems, strong  
research infrastructures, and extensive funding, such as the United States, South Korea, and the United Kingdom,  
tend to produce research that is widely cited and methodologically advanced. Conversely, countries such as  
Indonesia demonstrate growing engagement but lower global visibility, potentially due to challenges in funding,  
international collaboration, or indexing in high-impact journals.  
Thematically, dominant areas such as AI-driven personalisation, predictive analytics, customer engagement, and  
digital marketing transformation mirror current industry priorities. Organisations increasingly depend on AI to  
optimise decision-making and strengthen competitiveness, which in turn fuels academic attention. The frequent  
application of frameworks such as the Technology–Organization–Environment (TOE) and constructs like  
perceived usefulness further reflects their robustness and versatility in explaining the complexities of AI  
adoption across diverse contexts.  
Implications for Practice  
For Malaysia, the findings point to the importance of leveraging insights from both high-volume and high-impact  
research to inform AI adoption strategies. The TOE framework’s relevance is reaffirmed, emphasising that  
technological readiness, organisational capability, and environmental pressures collectively shape adoption  
outcomes.  
Managers can utilise insights from influential global studies to make informed decisions about selecting and  
integrating AI tools that enhance the customer experience, marketing efficiency, and overall competitiveness.  
Policymakers and business support agencies can draw on the results to guide the development of targeted training  
programmes, funding schemes, and digital infrastructure policies to strengthen adoption readiness among SMEs.  
Such efforts are essential to ensure that Malaysian organisations are well-positioned to compete in an  
increasingly AI-driven digital economy.  
Limitations and Opportunities for Future Research  
Despite offering valuable insights, the study has several limitations that present opportunities for further inquiry.  
First, the exclusive reliance on the Scopus database may introduce selection bias. Although Scopus is well-  
regarded, it may overlook high-quality research from emerging regions, practitioner-oriented outlets, and  
technical studies indexed in databases such as Web of Science, IEEE Xplore, or Google Scholar. Expanding  
database sources in future studies would reduce this bias and provide a more balanced representation of global  
research contributions.  
Second, the study focuses on publications from 2020 to 2025. While this timeframe captures recent  
developments, it may exclude foundational earlier work that shaped the evolution of AI marketing adoption.  
Extending the timeframe or conducting historical trend analyses could yield deeper insights into shifts in  
scholarly focus, methodological sophistication, and theoretical development.  
Third, although the study identifies dominant authors and countries, the current analysis does not fully unpack  
the socio-economic and institutional factors that enable certain regions to lead. High-income countries often  
benefit from stronger research ecosystems, advanced technological infrastructure, and greater funding  
availability, which naturally support higher research impact. Conversely, emerging markets may have lower  
output not because of a lack of expertise or relevance, but because of limited research visibility, reduced access  
Page 1696  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
to indexed publishing channels, or constraints on local research funding. Incorporating qualitative insights or  
cross-country comparisons in future research could offer a richer understanding of these disparities.  
CONCLUSION  
Restate the Purpose of the Study  
This study aimed to systematically explore and analyse the scholarly literature on the adoption of Artificial  
Intelligence (AI) marketing in Malaysia. Using a bibliometric approach, the study sought to identify publication  
trends, influential authors and institutions, leading journals, and emerging thematic areas in the field.  
Summary of Key Findings  
The bibliometric analysis revealed that research on AI adoption in marketing has grown substantially between  
2020 and 2025, reflecting increasing global interest in AI-enabled marketing practices. Computer science,  
business, management, and accounting emerged as the dominant disciplines, underscoring the field's  
interdisciplinary nature. Journals were the primary source type, while English was the dominant language of  
publication.  
At the global level, countries such as Indonesia, the United States, and Jordan showed the highest number of  
publications, although highly cited studies also originated from countries with fewer publications, highlighting  
the influence of quality over quantity. Leading authors and institutions were identified, providing a clear picture  
of the field's intellectual structure. The analysis also indicated emerging trends in AI marketing adoption, such  
as the integration of digital tools, increasing attention to organizational and environmental factors, and the role  
of perceived usefulness in mediating adoption decisions.  
Contributions to the Field  
This study contributes to the literature by providing a comprehensive, data-driven overview of current research  
on AI marketing adoption. It highlights key trends, influential contributors, and emerging topics, offering a  
foundational resource for scholars seeking to understand the field’s intellectual structure. Furthermore, the study  
extends the framework in the Malaysian context by integrating bibliometric insights with practical considerations  
for adoption, thereby bridging theoretical and applied perspectives.  
Implications for Practice  
The findings provide valuable guidance for managers, policymakers, and technology developers. For managers,  
understanding the factors that influence AI marketing adoption can support better decision-making, optimize  
technology implementation, and improve marketing outcomes. Policymakers can utilize these insights to design  
targeted support programs, financial incentives, and training initiatives that encourage AI adoption. Technology  
providers can leverage the identified trends to tailor AI marketing solutions to the needs, enhancing usability  
and perceived usefulness.  
Limitations and Future Directions  
The study is limited by its reliance on the Scopus database and the five-year publication window (2020–2025),  
which may exclude relevant literature from other databases or earlier periods. Future research could expand the  
dataset to include Web of Science, Google Scholar, and other regional sources, and consider a longer historical  
timeframe. Additionally, future studies could investigate the influence of emerging technologies, cybersecurity  
concerns, and sector-specific factors on AI marketing adoption. The use of mixed-methods research, longitudinal  
studies, and experimental designs could also enhance understanding of adoption dynamics and practical  
outcomes.  
Overall, this bibliometric analysis provides a clear, systematic overview of research on AI marketing adoption,  
highlighting influential authors, institutions, countries, and emerging trends. The study emphasizes the  
importance of AI in driving marketing innovation, operational efficiency, and competitive advantage in Malaysia.  
Page 1697  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
By identifying knowledge gaps and providing actionable insights, this research offers a roadmap for scholars,  
practitioners, and policymakers to advance the field and promote the successful adoption of AI marketing.  
ACKNOWLEDGEMENT  
The authors would like to express their sincere gratitude to the Ministry of Higher Education Malaysia (MOHE)  
for the financial support under the Fundamental Research Grant Scheme (FRGS-EC) [Grant No.:  
FRGS/1/2024/SS01/UTEM/03/3 (KPT) and FRGS-EC/1/2024/FPTT/F00604 (UTeM), and to Universiti  
Teknikal Malaysia Melaka (UTeM) for providing the research facilities and technical assistance  
throughout this study.  
REFERENCES  
1. Ahmi, A. (2023) ‘OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data’,  
11th International Conference on Applied Science and Technology 2022 (11th ICAST 2022) AIP  
Conference Proceedings, 2827, pp. 030006-1–030006-11. https://doi.org/10.1063/5.0164724  
2. Ahmi, A. (2024) biblioMagika. Available at: https://bibliomagika.com (Accessed: 13 November 2025).  
3. Aria, M. & Cuccurullo, C. (2017) ‘Bibliometrix: An R-tool for comprehensive science mapping analysis’,  
Journal of Informetrics, 11(4), pp. 959–975. https://doi.org/10.1016/j.joi.2017.08.007  
4. Aveyard, H. (2014) Doing a literature review in health and social care: A practical guide. 3rd edn.  
Maidenhead: Open University Press.  
5. Boubaker, S., Nguyen, D.K., Samet, S. & Vo, X.V. (2023) ‘Hybrid bibliometric-systematic review  
methodologies: Trends and best practices’, Scientometrics, 128(7), pp. 5027–5052.  
https://doi.org/10.1007/s11192-023-04678-9  
6. Creswell, J.W. & Poth, C.N. (2016) Qualitative inquiry and research design: Choosing among five  
approaches. 4th edn. Thousand Oaks: SAGE Publications.  
7. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W.M. (2021) ‘How to conduct a bibliometric  
analysis: An overview and guidelines’, Journal of Business Research, 133, pp. 285–296.  
https://doi.org/10.1016/j.jbusres.2021.04.070  
8. Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Rana, N.P. (2021)  
‘Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and  
agenda for research, practice and policy’, International Journal of Information Management, 57, 101994.  
https://doi.org/10.1016/j.ijinfomgt.2020.101994  
9. Fayad, A.A.S., Binti Mohd Ariff, A.H., Ooi, S.C., Ahmi, A. & Khatib, S.F.A. (2024) ‘Towards concise  
reporting through integrated reporting: A bibliometric review’, Meditari Accountancy Research, 32(3),  
pp. 832–856. https://doi.org/10.1108/MEDAR-10-2021-1470  
10. Goyal, S. & Kumar, S. (2021) ‘Financial literacy: A systematic review and bibliometric analysis’,  
International Journal of Consumer Studies, 45(1), pp. 80–105. https://doi.org/10.1111/ijcs.12605  
11. Hakim, T., Ahmi, A. & Alam, S. (2024) ‘A Decade in Blockchain: A Bibliometric Reflection on the  
Growth and Interdisciplinary Reach of a Disruptive Technology’, Journal of Information and  
Communication.  
12. Huang, M.H. & Rust, R.T. (2021) ‘A strategic framework for artificial intelligence in marketing’, Journal  
of the Academy of Marketing Science, 49, pp. 30–50. https://doi.org/10.1007/s11747-020-00749-9  
13. Kaartemo, V. & Helkkula, A. (2022) ‘Artificial intelligence in marketing: A systematic review of the  
literature’,  
https://doi.org/10.1016/j.jbusres.2022.01.001  
14. Kumar, V., Dixit, A., Javalgi, R.G. & Dass, M. (2023) ‘Artificial intelligence in marketing: Review and  
future research agenda’, Industrial Marketing Management, 110, pp. 1–18.  
https://doi.org/10.1016/j.indmarman.2022.07.003  
Journal  
of  
Business  
Research,  
145,  
pp.  
1022–1038.  
15. Lim, R.G., Voon, S.P., Yahya, F., Mohamad, F.S. & Ahmi, A. (2024) ‘Global and LMIC insights into  
…’ [full title needed], [Journal Name], [Volume(Issue)], pp. [pages].  
16. MacCoun, R.J. (1998) ‘Biases in the interpretation and use of research results’, Annual Review of  
Psychology, 49, pp. 259–287. https://doi.org/10.1146/annurev.psych.49.1.259  
Page 1698  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
17. Marinchak, C., Forrest, E. & Hoanca, B. (2018) ‘Marketing and artificial intelligence: An integrative  
review’,  
Journal  
of  
Strategic  
Marketing,  
26(5),  
pp.  
383–400.  
https://doi.org/10.1080/0965254X.2017.1407631  
18. Mikalef, P., Pappas, I.O., Krogstie, J. & Giannakos, M. (2021) ‘Investing in big data analytics capabilities  
for firm performance: A systematic review and future research directions’, Information & Management,  
58(8), 103440. https://doi.org/10.1016/j.im.2021.103440  
19. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G. & PRISMA Group (2009) ‘Preferred reporting items  
for systematic reviews and meta-analyses: The PRISMA statement’, PLoS Medicine, 6(7), e1000097.  
https://doi.org/10.1371/journal.pmed.1000097  
20. Ntakana, K. & Ahmi, A. (2024) ‘Blueprints for sustainability: An in-depth study of construction’, in  
Sustainable Development. IntechOpen. https://doi.org/10.5772/intechopen.1005619  
21. Punj, N., Ahmi, A., Tanwar, A. & Abdul Rahim, S. (2021) ‘Mapping the field of green manufacturing:  
A bibliometric review of the literature and research frontiers’, Journal of Cleaner Production, 423,  
138729. https://doi.org/10.1016/j.jclepro.2023.138729  
22. Sureka, A., Kumar, R. & Awasthi, A. (2022) ‘Integrating systematic review and bibliometric analysis  
for emerging technology research: A methodological perspective’, Scientometrics, 127(5), pp. 2961–  
2985. https://doi.org/10.1007/s11192-022-04342-w  
23. Tomar, S., Kumar, A., Kumar, P. & Singh, R. (2021) ‘Bibliometric mapping of AI in marketing research:  
Trends and future directions’, Journal of Business Research, 136, pp. 444–458.  
https://doi.org/10.1016/j.jbusres.2021.07.041  
24. Tranfield, D., Denyer, D. & Smart, P. (2003) ‘Towards a methodology for developing evidence-informed  
management knowledge by means of systematic review’, British Journal of Management, 14(3), pp. 207–  
222. https://doi.org/10.1111/1467-8551.00375  
25. Zoller, H. & Muldoon, J. (2020) ‘Systematic literature review as a method for social science research:  
Opportunities and challenges’, International Journal of Social Research Methodology, 23(5), pp. 485–  
500. https://doi.org/10.1080/13645579.2020.1727012  
Page 1699  
www.rsisinternational.org